| 引用本文: | 李景丽,时永凯,张琳娟,等.考虑电动汽车有序充电的光储充电站储能容量优化策略[J].电力系统保护与控制,2021,49(7):94-102.[点击复制] |
| LI Jingli,SHI Yongkai,ZHANG Linjuan,et al.Booster planning considering dynamic development of load and distributed generator for substations in low-load density areas[J].Power System Protection and Control,2021,49(7):94-102[点击复制] |
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| 摘要: |
| 针对电动汽车和光伏系统接入配电网与储能装置结合过程中的配置优化问题，提出了一种考虑电动汽车有序充电的光储充电站储能容量优化策略。首先，基于典型日光照强度曲线和光电能量转换关系计算光伏系统输出功率。其次，根据电动汽车用户出行习惯、充电行为特性、充电模式等充电负荷影响因素，建立影响电动汽车充电负荷的概率模型，利用蒙特卡洛方法预测无序充电下电动汽车充电负荷。然后，以电网出力曲线峰谷差最小为目标函数、采用粒子群算法计算电动汽车有序充电时电网出力总负荷，进而确定光储充电站储能容量最优解。最后，利用所提策略计算以电动私家车和电动出租车为主要服务对象的某居民区光储充电站内最优储能容量。结果表明，未考虑储能时电动汽车无序充电造成电网负荷峰上加峰，有序充电下电网负荷峰谷差值下降15.35%，考虑电动汽车有序充电同时配置最优储能容量时电网负荷峰谷差值下降了20.65%，实现了削峰填谷，增强了电力系统运行的稳定性。得到的结果为光储充电站的储能容量配置提供了参考。 |
| 关键词: 电动汽车 有序充电 光储充电站 容量优化 粒子群算法 |
| DOI：DOI: 10.19783/j.cnki.pspc.201296 |
| 投稿时间：2020-10-27修订日期：2021-01-26 |
| 基金项目:国家自然科学基金项目资助(51307152) |
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| Booster planning considering dynamic development of load and distributed generator for substations in low-load density areas |
| LI Jingli1,SHI Yongkai1,ZHANG Linjuan2,YANG Xuchen1,WANG Lili2,CHEN Xing3 |
| (1. School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China;
2. State Grid Henan Economic and Technological Research Institute, Zhengzhou 450000, China;
3. Shangqiu Power Supply Company, State Grid Henan Electric Power Company, Shangqiu 476000, China) |
| Abstract: |
| There is a configuration optimization problem in the process of integrating electric vehicles and photovoltaic systems into the distribution network and energy storage devices. Thus this paper proposes an energy storage capacity optimization strategy for photovoltaic storage charging stations that considers the orderly charging of electric vehicles. First, it calculates the output power of the photovoltaic system based on a typical daylight intensity curve and the photoelectric energy conversion relationship. Secondly, from charging load influencing factors such as the travel habits of electric vehicle users, charging behavior characteristics, charging mode and so on, a probability model that affects the charging load of electric vehicles is established, and the Monte Carlo method is used to predict the charging load under disorderly charging. Then taking the minimum peak-valley difference of the power grid output curve as the objective function, the particle swarm algorithm is used to calculate the total power grid output load during orderly charging, and it then determines the optimal solution for the energy storage capacity of an optical storage charging station. Finally, the strategy is used to calculate the optimal energy storage capacity in a residential area optical storage charging station with electric private cars and electric taxis as the main service objects. The results show that the disorderly charging of electric vehicles when energy storage is not considered causes the power grid load to add peaks. The peak-to-valley difference of the power grid load is reduced by 15.35% under orderly charging. When the orderly charging of electric vehicles is considered and the optimal energy storage capacity is configured, the peak-to-valley difference of power grid load decreases by 20.65%. This realizes peak shaving and valley filling, and enhances the stability of power system operation. The results obtained in this paper provide a reference for the energy storage capacity configuration of an optical storage charging station.
This work is supported by the National Natural Science Foundation of China (No. 51307152). |
| Key words: electric vehicle orderly charging optical storage and charging station capacity optimization particle swarm algorithm |
